{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic SEIRS Network Model Demo\n",
    "\n",
    "**This notebook provides a demonstration of the core functionality of the Basic SEIRS Network Model and offers a sandbox for easily changing simulation parameters and scenarios.** \n",
    "For a more thorough walkthrough of the model and use of this package, refer to the README."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Installing and Importing the model code\n",
    "All of the code needed to run the model is imported from the ```models``` module of this package.\n",
    "\n",
    "#### Install the package using ```pip```\n",
    "The package can be installed on your machine by entering this in the command line:\n",
    "\n",
    "```sudo pip install seirsplus```\n",
    "\n",
    "Then, the ```models``` module can be imported into your scripts as shown here:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from seirsplus.models import *\n",
    "from seirsplus.networks import *\n",
    "import networkx"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### *Alternatively, manually copy the code to your machine*\n",
    "*You can use the model code without installing a package by copying the ```models.py``` module file to a directory on your machine. In this case, the easiest way to use the module is to place your scripts in the same directory as the module, and import the module as shown here:*\n",
    "```python\n",
    "from models import *\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating interaction networks\n",
    "\n",
    "This model simulates SEIRS epidemic dynamics for populations with a structured interaction network (as opposed to standard deterministic SIR/SEIR/SEIRS models, which assume uniform mixing of the population). As such, a graph specifying the interaction network for the population must be specified, where each node represents an individual in the population and edges connect individuals who have regular interactions.\n",
    "\n",
    "The interaction network can be specified by a ```networkx``` Graph object or a 2D numpy array representing the adjacency matrix, either of which can be defined and generated by any method.\n",
    "\n",
    "*Here, we use a ```custom_exponential_graph()``` generation function included in this package, which generates power-law graphs that have degree distributions with two exponential tails. For more information on this custom graph type and its generation, see the README.*\n",
    "\n",
    "**_Note:_** *Simulation time increases with network size. Small networks simulate quickly, but have more stochastic volatility. Networks with ~10,000 are large enough to produce per-capita population dynamics that are generally consistent with those of larger networks, but small enough to simulate quickly. We recommend using networks with ~10,000 nodes for prototyping parameters and scenarios, which can then be run on larger networks if more precision is required (for more on this, see README).*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "numNodes = 10000\n",
    "baseGraph    = networkx.barabasi_albert_graph(n=numNodes, m=9)\n",
    "# Baseline normal interactions:\n",
    "G_normal     = custom_exponential_graph(baseGraph, scale=100)\n",
    "plot_degree_distn(G_normal, max_degree=40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Epidemic scenarios of interest often involve interaction networks that change in time. Multiple interaction networks can be defined and used at different times in the model simulation, as will be shown below.\n",
    "\n",
    "*Here we generate a graph representing interactions during corresponding to Social Distancing, where each individual drops some portion of their normal interactions with others. Again, we use the ```custom_exponential_graph()``` to generate this graph; for more information, see the README.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Social distancing interactions:\n",
    "G_distancing = custom_exponential_graph(baseGraph, scale=10)\n",
    "plot_degree_distn(G_distancing, max_degree=40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This SEIRS+ model features dynamics corresponding to testing individuals for the disease and moving individuals with detected infection into a state where their rate of recovery, mortality, etc may be different. In addition, given that this model considers individuals in an interaction network, a separate graph defining the interactions for individuals with detected cases can be specified.\n",
    "\n",
    "*Here we generate a graph representing the interactions that individuals have when they test positive for the disease. In this case, a significant portion of each individual's normal interaction edges are removed from the graph, as if the individual is quarantined upon detection of infection. Again, we use the ```custom_exponential_graph()``` to generate this graph; for more information, see the README.*\n",
    "\n",
    "For more information on how testing, contact tracing, and detected cases are handled in this model, see the README."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Quarantine interactions:\n",
    "G_quarantine = custom_exponential_graph(baseGraph, scale=5)\n",
    "plot_degree_distn(G_quarantine, max_degree=40)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Initializing the model parameters\n",
    "All model parameter values, including the normal and quarantine interaction networks, are set in the call to the ```SEIRSNetworkModel``` constructor. The normal interaction network ```G``` and the basic SEIR parameters ```beta```, ```sigma```, and ```gamma``` are the only required arguments. All other arguments represent parameters for optional extended model dynamics; these optional parameters take default values that turn off their corresponding dynamics when not provided in the constructor. For clarity and ease of customization in this notebook, all available model parameters are listed below. \n",
    "\n",
    "For more information on parameter meanings, see the README.\n",
    "\n",
    "*The parameter values shown correspond to very rough approximations of parameter values for the COVID-19 epidemic.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "SIGMA  = 1/5.2\n",
    "GAMMA  = 1/10\n",
    "MU_I   = 0.002\n",
    "\n",
    "R0     = 2.5\n",
    "BETA   = 1/(1/GAMMA) * R0\n",
    "BETA_Q = 0.5*BETA\n",
    "\n",
    "P      = 0.2\n",
    "Q      = 0.05"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = SEIRSNetworkModel(G       = G_normal, \n",
    "                          beta    = BETA, \n",
    "                          sigma   = SIGMA, \n",
    "                          gamma   = GAMMA, \n",
    "                          mu_I    = MU_I,\n",
    "                          mu_0    = 0, \n",
    "                          nu      = 0, \n",
    "                          xi      = 0,\n",
    "                          p       = P,\n",
    "                          G_Q     = G_quarantine, \n",
    "                          beta_Q  = BETA_Q, \n",
    "                          sigma_Q = SIGMA,\n",
    "                          gamma_Q = GAMMA, \n",
    "                          mu_Q    = MU_I,\n",
    "                          theta_E = 0, \n",
    "                          theta_I = 0, \n",
    "                          phi_E   = 0, \n",
    "                          phi_I   = 0, \n",
    "                          psi_E   = 1.0, \n",
    "                          psi_I   = 1.0,\n",
    "                          q       = Q,\n",
    "                          initI   = numNodes/100, \n",
    "                          initE   = 0, \n",
    "                          initQ_E = 0, \n",
    "                          initQ_I = 0, \n",
    "                          initR   = 0, \n",
    "                          initF   = 0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checkpoints\n",
    "Model parameters can be easily changed during a simulation run using checkpoints. A dictionary holds a list of checkpoint times (```checkpoints['t']```) and lists of new values to assign to various model parameters at each checkpoint time. Any model parameter listed in the model constrcutor can be updated in this way. Only model parameters that are included in the checkpoints dictionary have their values updated at the checkpoint times, all other parameters keep their pre-existing values.\n",
    "\n",
    "*The checkpoints shown here correspond to starting social distancing and testing at time ```t=20``` (the graph ```G``` is updated to ```G_distancing``` and locality parameter ```p``` is decreased to ```0.1```; testing params ```theta_E```, ```theta_I```, ```phi```, and ```phi_I``` are set to non-zero values) and then stopping social distancing at time ```t=100``` (```G``` and ```p``` changed back to their \"normal\" values; testing params remain non-zero).*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "checkpoints = {'t':       [20, 100], \n",
    "               'G':       [G_distancing, G_normal], \n",
    "               'p':       [0.5*P, P], \n",
    "               'theta_E': [0.02, 0.02], \n",
    "               'theta_I': [0.02, 0.02], \n",
    "               'phi_E':   [0.2, 0.2], \n",
    "               'phi_I':   [0.2, 0.2]}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Running the simulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t = 0.08\n",
      "t = 10.00\n",
      "[Checkpoint: Updating parameters]\n",
      "t = 20.00\n",
      "t = 30.00\n",
      "t = 40.01\n",
      "t = 50.01\n",
      "t = 60.00\n",
      "t = 70.01\n",
      "t = 80.01\n",
      "t = 90.01\n",
      "[Checkpoint: Updating parameters]\n",
      "t = 100.00\n",
      "t = 110.00\n",
      "t = 120.10\n",
      "t = 130.02\n",
      "t = 140.01\n",
      "t = 150.02\n",
      "t = 160.04\n",
      "t = 170.02\n",
      "t = 180.01\n",
      "t = 190.04\n",
      "t = 200.02\n",
      "t = 210.00\n",
      "t = 220.06\n",
      "t = 230.08\n",
      "t = 240.00\n",
      "t = 250.05\n",
      "t = 260.02\n",
      "t = 270.12\n",
      "t = 280.04\n",
      "t = 290.19\n",
      "t = 300.01\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.run(T=300, checkpoints=checkpoints)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualizing the results\n",
    "The ```SEIRSNetworkModel``` class has a ```plot()``` convenience function for plotting simulation results on a matplotlib axis. This function generates a line plot of the frequency of each model state in the population by default, but there are many optional arguments that can be used to customize the plot.\n",
    "\n",
    "The ```SEIRSNetworkModel``` class also has convenience functions for generating a full figure out of model simulation results (optionaly arguments can be provided to customize the plots generated by these functions). \n",
    "- ```figure_basic()``` calls the ```plot()``` function with default parameters to generate a line plot of the frequency of each state in the population.\n",
    "- ```figure_infections()``` calls the ```plot()``` function with default parameters to generate a stacked area plot of the frequency of only the infection states ($E$, $I$, $Q_E$, $Q_I$) in the population.\n",
    "\n",
    "For more information on the built-in plotting functions, see the README."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/site-packages/seirsplus/models.py:1538: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(['{:,.0%}'.format(y) for y in ax.get_yticks()])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(<Figure size 864x576 with 1 Axes>,\n",
       " <AxesSubplot:xlabel='days', ylabel='percent of population'>)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.figure_infections(vlines=checkpoints['t'], ylim=0.15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Reference simulation visualizations\n",
    "\n",
    "We can also visualize the results of other simulation(s) as a reference for comparison of our main simulation.\n",
    "\n",
    "Here we simulate a model where no distancing or testing takes place, so that we can compare the effects of these interventions:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t = 0.02\n",
      "t = 10.04\n",
      "t = 20.02\n",
      "t = 30.01\n",
      "t = 40.01\n",
      "t = 50.01\n",
      "t = 60.00\n",
      "t = 70.00\n",
      "t = 80.00\n",
      "t = 90.01\n",
      "t = 100.00\n",
      "t = 110.06\n",
      "t = 120.02\n",
      "t = 130.17\n",
      "t = 140.02\n",
      "t = 150.54\n",
      "t = 170.43\n",
      "t = 180.76\n",
      "t = 190.54\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ref_model = SEIRSNetworkModel(G=G_normal, beta=BETA, sigma=SIGMA, gamma=GAMMA, mu_I=MU_I, p=P,\n",
    "                              G_Q=G_quarantine, beta_Q=BETA_Q, sigma_Q=SIGMA, gamma_Q=GAMMA, mu_Q=MU_I,\n",
    "                              theta_E=0, theta_I=0, phi_E=0, phi_I=0, psi_E=1.0, psi_I=1.0, q=Q,\n",
    "                              initI=numNodes/100)\n",
    "ref_model.run(T=300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can visualize our main simulation together with this reference simulation by passing the model object of the reference simulation to the appropriate figure function argument (note: a second reference simulation could also be visualized by passing it to the ```dasheQ_reference_results``` argument):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/site-packages/seirsplus/models.py:1538: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(['{:,.0%}'.format(y) for y in ax.get_yticks()])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(<Figure size 864x576 with 1 Axes>,\n",
       " <AxesSubplot:xlabel='days', ylabel='percent of population'>)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.figure_infections(vlines=checkpoints['t'], shaded_reference_results=ref_model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As further demonstration, we might also wish to compare the results of these network model simulations to a deterministic model simulation of the same SEIRS parameters (with no interventions in this case):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "t = 299.90\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ref_model_determ = SEIRSModel(beta=BETA, sigma=SIGMA, gamma=GAMMA, mu_I=MU_I, initI=100, initN=10000) \n",
    "ref_model_determ.run(T=300)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.8/site-packages/seirsplus/models.py:1538: UserWarning: FixedFormatter should only be used together with FixedLocator\n",
      "  ax.set_yticklabels(['{:,.0%}'.format(y) for y in ax.get_yticks()])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(<Figure size 864x576 with 1 Axes>,\n",
       " <AxesSubplot:xlabel='days', ylabel='percent of population'>)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.figure_infections(vlines=checkpoints['t'], ylim=0.25, \n",
    "                        shaded_reference_results=ref_model, shaded_reference_label='network: no interventions',\n",
    "                        dashed_reference_results=ref_model_determ, dashed_reference_label='deterministic: no interventions')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
